The most useful paradigm in data analysis and machine learning is arguably the modeling of data by a low-dimensional subspace. The well-known total least squares solves this modeling problem by finding the subspace minimizing the sum of squared errors of data points. This is practically done via principal components analysis (PCA) of the data matrix. Nevertheless, this procedure is highly sensitive to outliers.